代写42028留学生、代做Neural Networks、Python代写、代做Python程序语言

日期：2019-05-11 11:18

42028: Assignment 2 – Autumn 2019 Page 1 of 4

Faculty of Engineering and Information Technology

School of Software

42028: Deep Learning and Convolutional Neural Networks

Autumn 2019

ASSIGNMENT-2 SPECIFICATION

Due date Friday 11:59pm, 31 May 2019

Demonstrations Optional, If required.

Marks 40% of the total marks for this subject

Submission 1. A report in PDF or MS Word document (10-pages)

2. Google Colab/iPython notebooks

Submit to UTS Online assignment submission

Note: This assignment is individual work.

Summary

This assessment requires you to customize the standard CNN architectures for

image classification. Standard CNNs such as AlexNet, GoogleNet, ResNet should be

used to create customized version of the architectures. Students are also required

to implement a custom CNN architecture for object detection and localization.

Both the customized CNNs (image classification and object detection) should be

trained and tested using the dataset provided.

Students need to provide the code (ipython Notebook) and a final report for the

assignment, which will outline a brief assumptions/intuitions considered to create

the customized CNNs and discuss the performance.

Assignment Objectives

The purpose of this assignment is to demonstrate competence in the following

skills.

To ensure that the student has a firm understanding of CNNs and object

detections algorithms. This will facilitate the learning of advanced topics for

research and also assist in completing the project.

To ensure that the student can develop custom CNN architectures for different

computer vision related tasks.

42028: Assignment 2 – Autumn 2019 Page 2 of 4

Tasks:

Description:

1. Customize AlexNet/GoogleNet/ResNet and reduce/increase the layers. Train

and test on image classification.

2. Implement a custom CNN architecture for object detection and localization.

3. Train and test the custom architecture on a given dataset for detection of

multiple Objects, using Faster RCNN or YOLO object detection methods.

Training, validation and testing datasets will be provided.

Write a short report on the implementation, linking the concepts and methods

learned in class, and also provide assumptions/intuitions considered to create the

custom CNNs. Provide diagrams for the CNNs architecture where required for

better illustrations. Provide the model summary, such as input and output

parameters, etc. Discuss the results clearly and explain the different

situations/constraints for the better understanding of the results obtained.

Dataset to be used: Provided separately.

Report Structure (suggestion only):

The report may include the following sections:

1. Introduction: Provide a brief outline of the report and also briefly explain

the baseline CNN architectures used to create the custom CNNs for image

classification and object detection.

2. Dataset: Provide a brief description of the dataset used with some sample

images of each class.

3. Proposed CNN architecture for Image classification:

a. Baseline architecture used.

b. Customized architecture

c. Assumptions/intuitions

d. Model summary

4. Proposed CNN architecture for Object Detection and localization:

a. Baseline architecture used.

b. Customized architecture

c. Assumptions/intuitions

d. Model summary

5. Experimental results and discussion:

a. Experimental settings:

i. Image classification

ii. Object detection

b. Experimental Results:

i. Image classification

ii. Object detection

iii. Discussion: Provide your understanding of the performance

and accuracy obtained. You may also include some image

samples which were wrongly classified.

42028: Assignment 2 – Autumn 2019 Page 3 of 4

6. Conclusion: Provide a short paragraph detailing your understanding of the

experiments and results.

Deliverables:

7. Project Report (10 pages max)

8. Google Colab or Ipython notebook, with the code

Additional Information:

Assessment Submission

Submission of your assignment is in two parts. You must upload a zip file of the

Ipython/Colab notebooks and Report to UTS Online. This must be done by the Due

Date. You may submit as many times as you like until the due date. The final

submission you make is the one that will be marked. If you have not uploaded your zip

file within 7 days of the Due Date, or it cannot be run in the lab, then your assignment

will receive a zeromark. Additionally, the result achieved and shown in the

ipython/Colab notebooks should match the report. Penalties apply if there are

inconsistencies in the experimental results and the report.

PLEASE NOTE 1: It is your responsibility to make sure you have thoroughly tested your

program to make sure it is working correctly.

PLEASE NOTE 2: Your final submission to UTS Online is the one that is marked. It does

not matter if earlier submissions were working; they will be ignored. Download your

submission from UTS Online and test it thoroughly in your assigned laboratory.

Return of Assessed Assignment

It is expected that marks will be made available 2 weeks after the submission via UTS

Online. You will be given a copy of the marking sheet showing a breakdown of the marks.

Queries

If you have a problem such as illness which will affect your assignment submission

contact the subject coordinator as soon as possible.

Dr. Nabin Sharma

Room: CB11.07.124

Phone: 9514 1835

Email: Nabin.Sharma@uts.edu.au

If you have a question about the assignment, please post it to the UTS Online forum

for this subject so that everyone can see the response.

If serious problems are discovered the class will be informed via an announcement on UTS

Online. It is your responsibility to make sure you frequently check UTS Online.

42028: Assignment 2 – Autumn 2019 Page 4 of 4

PLEASE NOTE: If the answer to your questions can be found directly in any of the

following

Subject outline

Assignmentspecification

UTS Online FAQ

UTS Online discussion board

You will be directed to these locations rather than given a direct answer.

Extensions and Special Consideration

In alignment with Faculty policies, assignments that are submitted after the Due Date

will lose 10% of the received grade for each day, or part thereof, that the assignment

is late. Assignments will not be accepted after 5 days after the Due Date.

When, due to extenuating circumstances, you are unable to submit or present an

assessment task on time, please contact your subject coordinator before the

assessment task is due to discuss an extension. Extensions may be granted up to a

maximum of 5 days (120 hours). In all cases, you should have extensions confirmed in

writing.

If you believe your performance in an assessment item or exam has been adversely

affected by circumstances beyond your control, such as a serious illness, loss or

bereavement, hardship, trauma, or exceptional employment demands, you may be

eligible to apply for Special Consideration (https://www.uts.edu.au/currentstudents/managing-your-course/classes-and-assessment/specialcircumstances/special).

Academic Standards and Late Penalties

Please refer to subject outline.